Summarization
Transformers
PyTorch
TensorFlow
JAX
Rust
English
bart
text2text-generation
Eval Results (legacy)
Instructions to use facebook/bart-large-xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/bart-large-xsum with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="facebook/bart-large-xsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-xsum") model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-xsum") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 346a254a7c8eb7557d2c4d48371d041f7db004b92fa2e5cb451a05b73884f444
- Size of remote file:
- 2.04 GB
- SHA256:
- 3706d7f9974a64867550bb5c12cb33fd75d5d60f2f5af0ecd149f684f0308121
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